fastNLP/fastNLP/modules/encoder/embedding.py

import torch.nn as nn


class Embedding(nn.Module):
    """
    A simple lookup table
    Args:
    nums : the size of the lookup table
    dims : the size of each vector
    padding_idx : pads the tensor with zeros whenever it encounters this index
    sparse : If True, gradient matrix will be a sparse tensor. In this case,
    only optim.SGD(cuda and cpu) and optim.Adagrad(cpu) can be used
    """

    def __init__(self, nums, dims, padding_idx=0, sparse=False, init_emb=None, dropout=0.0):
        super(Embedding, self).__init__()
        self.embed = nn.Embedding(nums, dims, padding_idx, sparse=sparse)
        if init_emb is not None:
            self.embed.weight = nn.Parameter(init_emb)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        x = self.embed(x)
        return self.dropout(x)